In many traditional payment systems, when a credit card or other type of payment card is used, a payment network will calculate a fraud score for the transaction based on transaction details and account data for the credit card account, and deliver the fraud score to an issuing bank that holds the account. The issuer is then free to decide to approve or decline the transaction based on the fraud score among other deciding factors, such as the amount of available credit for the account. If the issuer decides to decline the transaction, then the merchant may be notified and the transaction may be stopped unless the consumer presents an alternative payment method.
However, these existing fraud detection systems often rely on historical transaction data across a vast number of consumers and merchants in order to develop the algorithms and rules that are applied to transactions. The result is that the same algorithms are applied to transactions at a small, specialty business as they are at an international department store chain and designed based on data captured thereto. Because the transactions, consumers, products, and considerations for each merchant can vary greatly from merchant to merchant, using such broadly designed and applied fraud rules can be detrimental, leading to instances where fraud is not detected or a genuine transaction incorrectly identified as fraudulent. This can result in a loss of revenue for the merchants, acquirers, and issuers involved, and can also greatly convenience consumers in instances where a genuine transaction is indicated as fraudulent, which may adversely affect the ongoing consumer-merchant relationship.
Thus, there is a need for a technical system where fraud detection is based on merchant specific data. By generating fraud rules and algorithms that are specific to a merchant, more accurate fraud scores can be obtained, which can result in more effective fraud detection. In addition, by enabling merchants to accept the risk of a transaction based on the merchant specific fraud detection data, some merchants may be able to reduce the occurrence of false positive transactions being rejected, further increasing the effectiveness of the system and thereby increasing overall revenue for all parties involved and increasing the strength of the consumer-merchant relationship.